CN114371701B - Unmanned ship course control method, controller, autopilot and unmanned ship - Google Patents
Unmanned ship course control method, controller, autopilot and unmanned ship Download PDFInfo
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- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/0206—Control of position or course in two dimensions specially adapted to water vehicles
Abstract
The invention provides an unmanned ship course control method, a controller, an automatic rudder and an unmanned ship, which realize the online adjustment and optimization of an unmanned ship course PD control system and enable the control system to have self-adaptive capacity. The method comprises the following steps: step 1, constructing a second-order nonlinear response model suitable for high-speed navigation of the unmanned ship; step 2, building a course control system simulation platform and debugging control parameters; step 3, optimizing the PD controller into a fuzzy self-adaptive PD controller; step 4, designing input and output of a fuzzy controller suitable for unmanned ship course control and corresponding fuzzy domains and quantization factors according to the application range and control precision requirements of an actual unmanned ship course control system; step 5, carrying out fuzzification processing on the course deviation and the deviation change rate of the unmanned ship and the control parameter optimization quantity, and designing a triangular membership function of an input/output fuzzification variable of the fuzzy controller; and 6, designing a fuzzy optimization method of the unmanned ship course control process.
Description
Technical Field
The invention belongs to the field of ocean aircraft automation engineering and unmanned aerial vehicle automatic rudder, and particularly relates to an unmanned aerial vehicle course control method, a controller, an automatic rudder and an unmanned aerial vehicle.
Technical Field
The unmanned surface vehicle (Unmanned Surface Vessel, USV), which is an intelligent unmanned marine vehicle for short, is a hot spot for research in the unmanned marine vehicle field in the world in recent years; the unmanned ship has wide application prospect and huge commercial value in the aspects of military application, commercial development, scientific investigation and the like.
The unmanned ship needs to achieve a target navigation point and avoid obstacles in the process of executing tasks, and the unmanned ship needs to rely on better navigation automatic control capability. The better the steering response capability is, the better the unmanned ship can be applied to navigation obstacle avoidance and other functions. The course control system of the unmanned ship is a typical second-order nonlinear system, and in the high-speed navigation process, the influence of complex marine environment interference such as wind and wave current is greatly enhanced compared with that in the middle-low speed navigation, the stability and the accuracy of the course automatic control are ensured, and the reliability and the self-adaptability of the course automatic control system are very high.
The PD course automatic control method is one of important methods in the unmanned ship course control field, but has the following defects:
(1) The PD automatic control system has a good effect on controlling a linear system, but has a poor control effect on some systems with high degree of nonlinearity and large hysteresis.
(2) In the PD course control method, under the conditions of controlling a nonlinear system and having external interference, as the control parameters cannot be adjusted and corrected on line, the method has no self-adaptive capacity and cannot achieve the desired control effect.
(3) The PD control parameters are often irregular and circulated, and the common online parameter identification method is high in difficulty and poor in effect.
Disclosure of Invention
The invention aims to solve the problems, and aims to provide an unmanned ship course control method, a controller, an autopilot and an unmanned ship, which can realize the on-line adjustment and optimization of an unmanned ship course PD control system, so that the control system has self-adaption capability, has better stability and robustness for the control performance of a system with high nonlinearity degree and external environment interference, and improves the accuracy and reliability of course control.
In order to achieve the above object, the present invention adopts the following scheme:
< method >
The invention provides a course control method of an unmanned ship, which is characterized by comprising the following steps of:
step 1, constructing a second-order nonlinear response model suitable for unmanned ship high-speed navigation based on a ship second-order linear response mathematical model;
step 2, building a course control system simulation platform based on the second-order nonlinear response model, and debugging out control parameters;
the unmanned ship heading angle is solved by adopting the following formula:
wherein:
wherein k is 1 、k 2 、k 3 、k 4 Is the slope of the heading angle at different sampling points;in order to be the angle of the heading,r is the heading angular velocity, f is the heading angular differential equation, < >>For the current moment heading angle +.>For the heading angle at the previous moment, t is the sampling time of the system, h is the sampling interval, n is the sampling times, t 0 For the initial sampling instant +.>Is an initial heading angle;
according to a second-order nonlinear mathematical model of unmanned ship control response, a second derivative differential formula of the unmanned ship heading angular speed is obtained as follows:
second derivative of heading angular velocity of unmanned shipIntegrating to obtain a first order derivative of the heading angular velocity>Then, the first order derivative is integrated to obtain a heading angular velocity r, and then the heading angular velocity is integrated to obtain a heading angle +>
The unmanned ship PD control system is built up, and a group of control parameters for converging the system are debugged: k (K) P0 、
K d0 And recording;
step 3, optimizing the PD controller into a fuzzy self-adaptive PD controller:
wherein K is P And K d The parameters of the proportional term and the derivative term of the adaptive PD controller, K P0 And K d0 The parameters of initial proportional term and differential term of original PD control system setting, delta K P And DeltaK d Two outputs of the fuzzy controller respectively;
step 4, designing input and output of a fuzzy controller suitable for unmanned ship course control and corresponding fuzzy domains and quantization factors according to the application range and control precision requirements of an actual unmanned ship course control system;
two-input single-output two-dimensional fuzzy controllers are designed for optimizing PD control parameters, and the input and output of the designed fuzzy controllers are respectively unmanned ship heading angle deviation and deviation change rate thereof:ec=de/dt, the output of the corresponding fuzzy controller is Δk p And DeltaK d The method comprises the steps of carrying out a first treatment on the surface of the The basic argument of designing inputs e and ec of the fuzzy controller is [ -18,18]And [ -1,1]The method comprises the steps of carrying out a first treatment on the surface of the The corresponding input e and ec fuzzy domains are respectively defined as [ -6,6]、[-1,1]The corresponding quantization factors are 1/3 and 1 respectively, and the output delta K of the fuzzy controller is designed at the same time P And DeltaK d The fuzzy domains of (a) are [ -1,1 respectively]And [ -3,3]；
Step 5, carrying out fuzzification processing on the course deviation and the deviation change rate of the unmanned ship and the control parameter optimization quantity, and designing a triangular membership function of an input/output fuzzification variable of the fuzzy controller;
step 6, designing a fuzzy optimization method of the unmanned ship course control process:
TABLE 1 DeltaK P Fuzzy optimization method table
TABLE 2 DeltaK d Fuzzy optimization method table
And (3) performing fuzzy optimization on the control method according to the steps 1-6, and controlling the heading of the unmanned aerial vehicle.
Preferably, the unmanned ship course control method provided by the invention can also have the following characteristics: in the step 1, the system motion response model adopts an unmanned ship second-order nonlinear operation model to realize unmanned ship operability analysis in the high-speed navigation process:
wherein delta is rudder angle, r is heading angular velocity, parameter alpha is nonlinear term correction coefficient, T 1 ,T 2 ,T 3 Are all time parameters, are related to rudder response and course stability of the unmanned ship, and are T 1 And T 2 Smaller means faster unmanned boat response and better follow-up, T 3 The parameter item is used for increasing the angular acceleration of the initial revolution; k is rudder angle gain coefficient, and is static increment from delta to r.
Preferably, the unmanned ship course control method provided by the invention can also have the following characteristics: in step 5, the number of fuzzy subsets of fuzzy domains of input and output is designed to be 7, and fuzzy variables of input and output and fuzzy subsets are selected to be { negative large (NB), negative Medium (NM), negative Small (NS), zero (0), positive Small (PS), median (PM), positive large (PB) }; the continuous control input and output of the fuzzy controller are quantized to the quantization domain, and the inputs e and ec are quantized to { -6, -4, -2,0,2,4,6} and { -1, -2/3, -1/3,0,1/3,2/3,1}, respectively, and the output ΔK is outputted P And DeltaK d Quantized to { -1, -2/3, -1/3,0,1/3,2/3,1} and { -3, -2, -1,0,1,2,3} respectively, the membership functions of the input-output fuzzy subset are designed to be triangular membership functions based on the data.
< controller >
Further, the present invention also provides an unmanned ship controller for controlling the heading of an unmanned ship using the method described in < method > above.
< Rudder >
Further, the present invention also provides an autopilot having the unmanned boat controller described in < controller > above.
< unmanned boat >
Still further, the present invention also provides an unmanned boat having the rudder described in < rudder > above.
Effects and effects of the invention
Aiming at the problems that the control parameters of the traditional PD course control method cannot be adjusted on line and have no self-adaptive capacity, and the nonlinearity degree is high, the hysteresis is strong and the control effect is poor under the working condition of external interference, the invention provides an unmanned ship course control method, a fuzzy PD controller, an autopilot and an unmanned ship, wherein in the specific design process of the fuzzy controller: firstly, carrying out amplitude limiting treatment on an actual domain to enable control to be more accurate, secondly, selecting a triangular type by a membership function to reduce the operation amount of a controller to improve efficiency, and finally, according to actual data and long-term experimental study summary, providing a brand-new control method which has better self-adaption capability and stronger anti-interference capability, can effectively enhance the stability and reliability of system control, and further improve efficiency and accuracy; based on the improved design, the fuzzy controller carries out online adjustment and optimization on PD control parameters, so that the operation amount of the controller is practically reduced, the control precision, stability and robustness are improved, and the accurate, efficient, stable and reliable control on the unmanned ship heading with complex working conditions and high nonlinearity degree is realized.
Drawings
FIG. 1 is a schematic diagram of PD course control according to an embodiment of the present invention;
fig. 2 is a schematic diagram of automatic fuzzy PD control according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a fuzzy PD controller design in accordance with an embodiment of the present invention;
FIG. 4 is a graph of an input bias e membership function according to an embodiment of the present invention;
FIG. 5 is a graph showing a distribution of the membership function of the input bias change rate ec according to an embodiment of the present invention;
FIG. 6 is an output ΔK according to an embodiment of the present invention P A membership function distribution diagram;
FIG. 7 is a graph showing the output ΔK according to an embodiment of the present invention d A membership function distribution diagram;
FIG. 8 is a graphical representation of unmanned ship heading angle comparisons for two control systems according to an embodiment of the present invention;
figure 9 is a comparison of unmanned rudder angles of two control systems according to an embodiment of the present invention;
fig. 10 is a diagram showing a change in control parameters of the fuzzy PD according to the embodiment of the present invention.
Detailed Description
The following describes in detail specific embodiments of the unmanned aerial vehicle heading control method, the controller, the autopilot and the unmanned aerial vehicle according to the present invention with reference to the accompanying drawings.
< example >
The unmanned ship course control method provided by the embodiment comprises the following steps:
step 1: and constructing a second-order nonlinear response model suitable for unmanned ship high-speed navigation based on the ship second-order linear response mathematical model.
Considering the influence of integral term on control effect lag and actuator saturation, the invention selects position PD control, namely proportional and differential control:
u(k)＝K p e(k)+K d [e(k)-e(k-1)]
the specific working principle is shown in figure 1, and the input of the controller is that Is the current heading angle of the unmanned ship, +.>Is the target heading angle of the unmanned ship; the output u is the rudder angle delta of the unmanned boat; k is the sampling frequency of the system, K p 、K d The proportional and integral term parameters, respectively. And rudder angle is output by the controller to rudder the unmanned ship, the deviation e is updated, and the deviation e is fed back to the controller for input until the error e=0, and the control is finished.
The system motion response model adopts an unmanned ship second-order nonlinear operation model, and a nonlinear term alpha r is added in consideration of the operation of the unmanned ship in a high-speed environment 3 Correction is carried out to realize unmanned ship operability analysis in the high-speed navigation process:
wherein delta is rudder angle; r is the angular velocity of the heading; the parameter alpha is a nonlinear term correction coefficient, and can be determined by a spiral or inverse spiral experimental result; t (T) 1 ,T 2 ,T 3 Are all time parameters, are calculated by adding various linear hydrodynamic derivatives corresponding to small r, and are related to rudder response and course stability of unmanned ship, and are generally T 1 And T 2 Smaller, T 3 Larger indicates faster response and better follow-up of the unmanned boat, except for T 1 And T 2 Is to make transient quickly attenuate motion to stable revolution, T 3 The parameter items are mainly used for increasing the angular acceleration of the initial revolution; k is the rudder angle gain coefficient, is the static increase from delta to r, is called the gyration index, and is also calculated by the relevant hydrodynamic derivative. T (T) 1 ,T 2 ,T 3 And K may refer to the associated vessel maneuvering book.
The specific parameters of the model constructed in this example are shown in table 3 below:
TABLE 3 motion model parameters
Step 2: and building a course control system simulation platform based on the second-order nonlinear response model, and debugging out control parameters.
The unmanned ship second-order nonlinear response model gives a nonlinear relation between a rudder angle and a heading angle, a first-order derivative of the heading angle speed and a second-order derivative of the heading angle speed, and the heading angle speed needs to be integrated in order to obtain the unmanned ship heading angle; integrating the first derivative of the bow angular velocity before integrating the bow angular velocity; and integrating the second derivative to obtain a first derivative before integrating the first derivative of the heading angular speed. In the simulation platform, the following method is adopted for numerical solution, and heading angle calculation is taken as an example:
wherein:
wherein k is 1 、k 2 、k 3 、k 4 Is the slope of the heading angle at different sampling points;is the heading angle, r is the heading angular velocity, f is the heading angle differential equation,/>For the current moment heading angle +.>For the heading angle at the previous moment, t is the sampling time of the system, h is the sampling interval, n is the sampling times, t 0 For the initial sampling instant +.>Is the initial heading angle.
According to a second-order nonlinear mathematical model of unmanned ship control response, obtaining a second derivative differential expression of the unmanned ship heading angular speed as follows:
integrating the object: second derivative of heading angular velocity of unmanned shipIntegration of the Dragon's base tower to obtain the first derivative of heading angle speed>Then, the first order derivative is integrated to obtain a heading angular velocity r, and then the heading angular velocity is integrated to obtain a heading angle +>
After the unmanned ship PD control system is built, a group of control parameters which can enable the system to converge, the overshoot is 4.8 degrees and the convergence time is 85s are debugged: k (K) P0 ＝1、K d0 =5 and record, lay a foundation for subsequent work.
Step 3: and designing a fuzzy controller based on a novel fuzzy control method to optimize an unmanned ship heading self-adaptive PD control principle frame so as to realize self-adaptive adjustment of control parameters of a heading control system along with the change of external environment.
The PD course control system has poor control effect under the conditions of complex and changeable external environment facing the setting of parameters and strong nonlinearity of control objects, and the parameters need to be continuously adjusted in order to obtain better control effect. But these parameters tend to be very variable and not have a fixed mathematical model and law. The invention provides a practical and simple adjusting method by introducing a novel optimizing fuzzy control method and utilizing a fuzzy controller to optimize control parameters on line, and the specific working principle is shown in figure 2. On the basis of the position type PD control, a novel fuzzy control method is adopted in a control system to design a fuzzy controller so as to realize on-line optimization adjustment of PD control parameters set by an original PD controller, and the PD controller is optimized into a fuzzy self-adaptive PD controller:
wherein K is P And K d The parameters of the proportional term and the derivative term of the adaptive PD controller, K P0 And K d0 The parameters of initial proportional term and differential term of original PD control system setting, delta K P And DeltaK d The two outputs of the fuzzy controller are respectively the variable quantity of the proportional and integral control parameters.
The design principle of the unmanned aerial vehicle fuzzy PD controller is shown in figure 3, and specifically comprises the following steps 3-6.
Step 4: according to the application range and the control precision requirement of the practical unmanned ship course control system, the input and output of the fuzzy controller suitable for unmanned ship course control and the corresponding fuzzy domain and quantization factors are designed.
Two-input single-output two-dimensional fuzzy controllers are designed for optimizing PD control parameters, and the input and output of the designed fuzzy controllers are respectively unmanned ship heading angle deviation and deviation change rate thereof:ec=de/dt, the input of the corresponding fuzzy controllerLet DeltaK p And DeltaK d The method comprises the steps of carrying out a first treatment on the surface of the The basic argument of designing inputs e and ec of the fuzzy controller is [ -18,18]And [ -1,1]Namely, when the deviation is larger than 18, the deviation is 18, and when the deviation is smaller than-18, the deviation is-18; the corresponding input e and ec fuzzy domains are respectively defined as [ -6,6]、[-1,1]The corresponding quantization factors are 1/3 and 1 respectively, and the output delta K of the fuzzy controller is designed at the same time P And DeltaK d The fuzzy domains of (a) are [ -1,1 respectively]And [ -3,3]。
Step 5: and carrying out fuzzification processing on the course deviation and the deviation change rate of the unmanned ship and the control parameter optimization quantity, and designing a triangular membership function of the input/output fuzzification variable of the fuzzy controller.
Designing the number of fuzzy subsets of fuzzy arguments of input and output to be 7, and selecting fuzzy variables of input and output and fuzzy subsets to be { negative large (NB), negative Medium (NM), negative Small (NS), zero (0), positive Small (PS), median (PM), positive large (PB) }; the continuous control input and output of the fuzzy controller are quantized to the quantization domain, and the inputs e and ec are quantized to { -6, -4, -2,0,2,4,6} and { -1, -2/3, -1/3,0,1/3,2/3,1}, respectively, and the output ΔK is outputted P And DeltaK d Quantized to { -1, -2/3, -1/3,0,1/3,2/3,1} and { -3, -2, -1,0,1,2,3} respectively, the membership functions of the input/output fuzzy subset are designed to be triangular membership functions based on the data, the membership function image of the fuzzy controller input e is shown in FIG. 4, the membership function image of the input ec is shown in FIG. 5, and the fuzzy controller output ΔK is shown in FIG. 5 P The membership function image of (1) is shown in FIG. 6, and ΔK is output d The membership function image of (2) is shown in figure 7.
Step 6: the fuzzy optimization method for the unmanned ship course control process is designed:
summarizing the corresponding relation between the change of unmanned ship course control deviation and deviation change rate and the change of control parameters according to the debugging experience, and designing a fuzzy optimization method of the unmanned ship course control process based on the relation.
Based on a large amount of unmanned ship heading control test study data and actual heading control accuracy, for two-input single-output fuzzy controllers, each fuzzy controller can design 7×7=49 fuzzy optimization methods as shown in tables 1 and 2 above.
The specific control method can be expressed in the following form:
(01)If(e＝NB)and(ec＝NB)then(ΔKp＝PB)(ΔKd＝NB)
(02)If(e＝NB)and(ec＝NM)then(ΔKp＝PB)(ΔKd＝NB)
(03)If(e＝NB)and(ec＝NS)then(ΔKp＝PB)(ΔKd＝NB)
(04)If(e＝NB)and(ec＝0)then(ΔKp＝PB)(ΔKd＝NB)
(05)If(e＝NB)and(ec＝PS)then(ΔKp＝PB)(ΔKd＝NB)
(06)If(e＝NB)and(ec＝PM)then(ΔKp＝PB)(ΔKd＝NB)
(07)If(e＝NB)and(ec＝PB)then(ΔKp＝PM)(ΔKd＝NM)
(08)If(e＝NM)and(ec＝NB)then(ΔKp＝PB)(ΔKd＝NB)
(09)If(e＝NM)and(ec＝NM)then(ΔKp＝PB)(ΔKd＝NB)
(10)If(e＝NM)and(ec＝NS)then(ΔKp＝PB)(ΔKd＝NM)
(11)If(e＝NM)and(ec＝0)then(ΔKp＝PB)(ΔKd＝NS)
(12)If(e＝NM)and(ec＝PS)then(ΔKp＝PM)(ΔKd＝NS)
(13)If(e＝NM)and(ec＝PM)then(ΔKp＝PM)(ΔKd＝NS)
(14)If(e＝NM)and(ec＝PB)then(ΔKp＝PM)(ΔKd＝0)
(15)If(e＝NS)and(ec＝NB)then(ΔKp＝PS)(ΔKd＝0)
(16)If(e＝NS)and(ec＝NM)then(ΔKp＝0)(ΔKd＝0)
(17)If(e＝NS)and(ec＝NS)then(ΔKp＝NS)(ΔKd＝PS)
(18)If(e＝NS)and(ec＝0)then(ΔKp＝NS)(ΔKd＝PM)
(19)If(e＝NS)and(ec＝PS)then(ΔKp＝NS)(ΔKd＝PM)
(20)If(e＝NS)and(ec＝PM)then(ΔKp＝NS)(ΔKd＝PM)
(21)If(e＝NS)and(ec＝PB)then(ΔKp＝NS)(ΔKd＝PM)
(22)If(e＝0)and(ec＝NB)then(ΔKp＝NB)(ΔKd＝PB)
(23)If(e＝0)and(ec＝NM)then(ΔKp＝NM)(ΔKd＝PM)
(24)If(e＝0)and(ec＝NS)then(ΔKp＝NM)(ΔKd＝PM)
(25)If(e＝0)and(ec＝0)then(ΔKp＝NS)(ΔKd＝PM)
(26)If(e＝0)and(ec＝PS)then(ΔKp＝NS)(ΔKd＝PS)
(27)If(e＝0)and(ec＝PM)then(ΔKp＝0)(ΔKd＝PS)
(28)If(e＝0)and(ec＝PB)then(ΔKp＝0)(ΔKd＝0)
(29)If(e＝PS)and(ec＝NB)then(ΔKp＝NS)(ΔKd＝PM)
(30)If(e＝PS)and(ec＝NM)then(ΔKp＝NS)(ΔKd＝PM)
(31)If(e＝PS)and(ec＝NS)then(ΔKp＝NS)(ΔKd＝PM)
(32)If(e＝PS)and(ec＝0)then(ΔKp＝NS)(ΔKd＝PM)
(33)If(e＝PS)and(ec＝PS)then(ΔKp＝NS)(ΔKd＝PS)
(34)If(e＝PS)and(ec＝PM)then(ΔKp＝0)(ΔKd＝0)
(35)If(e＝PS)and(ec＝PB)then(ΔKp＝PS)(ΔKd＝0)
(36)If(e＝PM)and(ec＝NB)then(ΔKp＝PB)(ΔKd＝0)
(37)If(e＝PM)and(ec＝NM)then(ΔKp＝PB)(ΔKd＝0)
(38)If(e＝PM)and(ec＝NS)then(ΔKp＝PB)(ΔKd＝NS)
(39)If(e＝PM)and(ec＝0)then(ΔKp＝PB)(ΔKd＝NS)
(40)If(e＝PM)and(ec＝PS)then(ΔKp＝PM)(ΔKd＝NM)
(41)If(e＝PM)and(ec＝PM)then(ΔKp＝PM)(ΔKd＝NB)
(42)If(e＝PM)and(ec＝PB)then(ΔKp＝PM)(ΔKd＝NB)
(43)If(e＝PB)and(ec＝NB)then(ΔKp＝PM)(ΔKd＝NM)
(44)If(e＝PB)and(ec＝NM)then(ΔKp＝PB)(ΔKd＝NB)
(45)If(e＝PB)and(ec＝NS)then(ΔKp＝PB)(ΔKd＝NB)
(46)If(e＝PB)and(ec＝0)then(ΔKp＝PB)(ΔKd＝NB)
(47)If(e＝PB)and(ec＝PS)then(ΔKp＝PB)(ΔKd＝NB)
(48)If(e＝PB)and(ec＝PM)then(ΔKp＝PB)(ΔKd＝NB)
(49)If(e＝PB)and(ec＝PB)then(ΔKp＝PB)(ΔKd＝NB)
further, by using the method for the unmanned ship controller, the heading of the unmanned ship can be automatically controlled.
Based on the above, further determining a defuzzification method to obtain a corresponding fuzzy controller output proportion factor: in this embodiment, a Mamdeni fuzzy inference method is adopted, fuzzy intersection of the corresponding fuzzy subsets is small, fuzzy or large, fuzzy inference is small, fuzzy clustering output is large, an output fuzzy subset is obtained through a fuzzy inference machine, and is converted into a clear quantity through defuzzification, the embodiment selects a defuzzification method as an area barycenter method, after the accurate quantity is obtained, the value range is determined by the fuzzy subset of a fuzzy output argument, transformation is performed through a scale factor, and ΔK is output P And DeltaK d The scale factors of (2) are 0.55 and 6.2, respectively.
The fuzzy self-adaptive PD controller of the unmanned ship, which comprises fuzzy subset membership function distribution, a novel fuzzy optimization method, a fuzzy reasoning method and defuzzification, is designed, and lays a foundation for the establishment of a novel automatic rudder and an unmanned ship under the subsequent fuzzy optimization effect.
The simulation results of the unmanned ship heading control effect using the prior art control method (PD control) and the improved control method of the present invention (fuzzy adaptive PD control) are compared, and the results are shown in fig. 8 to 10. The comparison of various indexes of the heading control performance under the two unmanned ship heading control methods is summarized in the following table 4:
table 4 comparison of unmanned ship heading control System Performance under two control methods
From the data in table 4, it can be analytically found that: for overshoot, the overshoot of the PD control system reaches 4.8 degrees, and the overshoot of the fuzzy self-adaptive PD control method is almost zero; for the response speed, although the time for reaching the target expected value is almost the same, the response speed of the adaptive control system in the early stage is obviously increased. For the speed of the system reaching the stable state, the stability time of the fuzzy PD control system is 25s, the time required by the classical control system is as long as 110s, and the stability of the improved control method is obviously higher than that of the traditional control method. The change of the self-adaptive PD control effect is just that the fuzzy controller carries out on-line adjustment on the initially set control parameters, so that the self-adaptation of the improved control method is reflected, and the self-adaptation accords with the expected control effect.
The above embodiments are merely illustrative of the technical solutions of the present invention. The unmanned ship heading control method, the controller, the autopilot and the unmanned ship according to the present invention are not limited to the above embodiments, but the scope of the invention is defined by the claims. Any modifications, additions or equivalent substitutions made by those skilled in the art based on this embodiment are within the scope of the invention as claimed in the claims.
Claims (5)
1. The unmanned ship course control method is characterized by comprising the following steps of:
step 1, constructing a second-order nonlinear response model suitable for unmanned ship high-speed navigation based on a ship second-order linear response mathematical model, and realizing unmanned ship maneuverability analysis in the high-speed navigation process:
wherein delta is rudder angle, r is heading angular velocity, parameter alpha is nonlinear term correction coefficient, T 1 ,T 2 ,T 3 Are all time parameters, and have rudder response and course stability with unmanned shipsClose, T 1 And T 2 Smaller means faster unmanned boat response and better follow-up, T 3 The parameter item is used for increasing the angular acceleration of the initial revolution; k is rudder angle gain coefficient;
step 2, building a course control system simulation platform based on the second-order nonlinear response model, and debugging out control parameters;
the unmanned ship heading angle is solved by adopting the following formula:
wherein:
wherein k is 1 、k 2 、k 3 、k 4 Is the slope of the heading angle at different sampling points;is the heading angle, r is the heading angular velocity, f is the heading angle differential equation,/>For the current moment heading angle +.>For the heading angle at the previous moment, t is the sampling time of the system, h is the sampling interval, n is the sampling times, t 0 For the initial sampling instant +.>Is an initial heading angle;
according to a second-order nonlinear mathematical model of unmanned ship control response, a second derivative differential formula of the unmanned ship heading angular speed is obtained as follows:
second derivative of heading angular velocity of unmanned shipIntegrating to obtain a first order derivative of the heading angular velocity>Then, the first order derivative is integrated to obtain a heading angular velocity r, and then the heading angular velocity is integrated to obtain a heading angle +>
The unmanned ship PD control system is built up, and a group of control parameters for converging the system are debugged: k (K) P0 、K d0 And recording;
step 3, optimizing the PD controller into a fuzzy self-adaptive PD controller:
wherein K is P And K d The parameters of the proportional term and the derivative term of the adaptive PD controller, K P0 And K d0 The parameters of initial proportional term and differential term of original PD control system setting, delta K P And DeltaK d Two outputs of the fuzzy controller respectively;
step 4, designing input and output of a fuzzy controller suitable for unmanned ship course control and corresponding fuzzy domains and quantization factors according to the application range and control precision requirements of an actual unmanned ship course control system;
two-input single-output two-dimensional fuzzy controllers are designed for optimizing PD control parameters, and the input and output of the designed fuzzy controllers are respectively unmanned ship heading angle deviation and deviation change rate thereof:ec=de/dt, the output of the corresponding fuzzy controller is Δk p And DeltaK d The method comprises the steps of carrying out a first treatment on the surface of the The basic argument of designing inputs e and ec of the fuzzy controller is [ -18,18]And [ -1,1]The method comprises the steps of carrying out a first treatment on the surface of the The corresponding input e and ec fuzzy domains are respectively defined as [ -6,6]、[-1,1]The corresponding quantization factors are 1/3 and 1 respectively, and the output delta K of the fuzzy controller is designed at the same time P And DeltaK d The fuzzy domains of (a) are [ -1,1 respectively]And [ -3,3]；
Step 5, carrying out fuzzification processing on the course deviation and the deviation change rate of the unmanned ship and the control parameter optimization quantity, and designing a triangular membership function of an input/output fuzzification variable of the fuzzy controller;
step 6, designing a fuzzy optimization method of the unmanned ship course control process:
TABLE 1 DeltaK P Fuzzy optimization method table
TABLE 2 DeltaK d Fuzzy optimization method table
NB represents negative big, NM represents negative middle, NS represents negative small, 0 represents zero, PS represents positive small, PM represents positive middle, PB represents positive big;
and (3) performing fuzzy optimization on the control method according to the steps 1-6, and controlling the heading of the unmanned aerial vehicle.
2. The unmanned aerial vehicle heading control method of claim 1, wherein:
in step 5, the number of fuzzy subsets of fuzzy arguments of input and output is designed to be 7, and fuzzy variables of input and output and the fuzzy subsets are selected to be { negative large (NB), negative Medium (NM), negative Small (NS), zero (0), positive Small (PS), median (PM) and positive large (PB) }; the continuous control input and output of the fuzzy controller are quantized to the quantization domain, and the inputs e and ec are quantized to { -6, -4, -2,0,2,4,6} and { -1, -2/3, -1/3,0,1/3,2/3,1}, respectively, and the output ΔK is outputted P And DeltaK d Quantized to { -1, -2/3, -1/3,0,1/3,2/3,1} and { -3, -2, -1,0,1,2,3} respectively, the membership function of the fuzzy subset is a triangular membership function.
3. An unmanned ship controller for controlling unmanned ship heading by using the unmanned ship heading control method as claimed in any one of claims 1 to 2.
4. An autopilot having the unmanned boat controller of claim 3.
5. An unmanned boat having the rudder of claim 4.
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